cplearn: A novel inference tool for single-cell RNA-seq data analysis

Example run-through with CIFAR10-noisy-clip-embedding.

The UMAP of the embeddings look as follows (note the overlaps):

#Load CoreSPECT and configuration module
from cplearn.corespect import CorespectModel
from cplearn.corespect.config import CoreSpectConfig

#Initial parameters.
cfg = CoreSpectConfig(
    q=40,               #Determines neighborhood size for the underlying q-NN graph
    r=20,               #Neighborhood radius parameter for ascending random walk with FlowRank
    core_frac=0.25,      #Fraction of points in the top-layer
    densify=False,      #Densifying different parts of the data to reduce fragmentation
    granularity=0.5,    #Higher granularity finds more local cores but can lead to missing out on weaker clusters.
    resolution=0.5      #Resolution for clustering with Leiden (more clustering methods will be added later)
).configure()


# Run **CoreSPECT**
model = CorespectModel(X, **cfg.unpack()).run(fine_grained=True,propagate=True)

'''
Main components of model:
model.layers_: Containts a list of lists. Each list consists of a subset of indices (between 0 and n-1, where n:= X.shape[0])
The first list corresponds to the indices that form the cores, the subsequent lists contain the outer layers.

model.labels_: n-sized integer array.
    If propagate==False: Contains clustering label for the core (model.layers_[0]) indices, -1 in other places.
    If propagate==True:  Contains clustering label for all the points.

'''

#Visualizing the outcomes:

#Step 1: Generate UMAP skeleton.
import umap
reducer=umap.UMAP()
X_umap=reducer.fit_transform(X)

Next, we present the layer extraction with a new visualization tool (we use the labels from model.labels_)

#Step 2: Initiate the **coremap** module.
from cplearn.coremap import Coremap
cmap=Coremap(model,global_umap=X_umap,fast_view=True)

'''
If fast_view= True, then we just use the UMAP skeleton, and then later show the visualization in a layer-wise manner.
If fast_view==False, we generate our own layer-wise visualization with the coremap algorithm.
'''


#Step 3: Layer-wise visualization (you can use your own labels instead of model.labels_)
from cplearn.coremap.vizualizer import visualize_coremap
fig=visualize_coremap(cmap,model.labels_, use_webgl=True)
fig.show()